báo cáo hóa học:" Development of the ATAQ-IPF: a tool to assess quality of life in IPF" doc

The pattern of correlations between ATAQ-IPF scores and physiologic variables known to be important in IPF, along with significant differences in ATAQ-IPF scores between subjects using v

R E S E A R C H Open Access

Development of the ATAQ-IPF: a tool to assess quality of life in IPF

Jeffrey J Swigris1*, Sandra R Wilson2, Kathy E Green3, David B Sprunger1, Kevin K Brown1, Frederick S Wamboldt4

Abstract

Background: There is no disease-specific instrument to assess health-related quality of life (HRQL) in patients with idiopathic pulmonary fibrosis (IPF)

Methods: Patients’ perspectives were collected to develop domains and items for an IPF-specific HRQL instrument

We used item variance and Rasch analysis to construct the ATAQ-IPF (A Tool to Assess Quality of life in IPF)

Results: The ATAQ-IPF version 1 is composed of 74 items comprising 13 domains All items fit the Rasch model Domains and the total instrument possess acceptable psychometric characteristics for a multidimensional

questionnaire The pattern of correlations between ATAQ-IPF scores and physiologic variables known to be

important in IPF, along with significant differences in ATAQ-IPF scores between subjects using versus those not using supplemental oxygen, support its validity

Conclusions: Patient-centered and careful statistical methodologies were used to construct the ATAQ-IPF version

1, an IPF-specific HRQL instrument Simple summation scoring is used to derive individual domain scores as well as

a total score Results support the validity of the ATAQ-IPF, and future studies will build on that validity

Introduction

Patient reported outcomes (PRO), such as quality of life

(QOL) or health-related QOL (HRQL), are commonly

used endpoints in clinical studies and therapeutic trials

in patients with pulmonary diseases Instruments that

assess PRO focus on the perceptions of patients with

the condition of interest; as such, they generate

mean-ingful data on disease effects not captured by other

out-come measures

HRQL instruments are generic or disease-specific The

merit of disease-specific instruments is that they contain

only items pertinent to patients with the disease of

interest Because of this, disease-specific instruments

tend to be more responsive than generic instruments to

underlying change Disease-specific HRQL instruments

have been developed for a number of pulmonary

condi-tions, including chronic obstructive pulmonary disease

[1-3] and asthma,[4,5] but not for idiopathic pulmonary

fibrosis (IPF)

IPF is a progressive, fibrosing, parenchymal lung

dis-ease[6] with distinctive pathophysiological processes IPF

has no reliably effective therapy, and survival rates are worse than for many cancers [7] In people with IPF, dyspnea limits physical activity, and hypoxemia ulti-mately develops, requiring patients to use supplemental oxygen Given these discomforting aspects and the poor survival rates, it is not surprising that generic HRQL in patients with IPF is impaired [8,9] Because IPF lacks a cure, there is a great deal of interest in maintaining or improving HRQL, so patients can live with acceptable QOL for however long they survive Without a disease-specific instrument, there will continue to be uncertainty regarding whether relevant aspects and effects of the disease are being measured adequately and whether drug therapies, or other interventions, have a net benefi-cial or adverse impact on HRQL In this manuscript, we report on the development an IPF-specific HRQL instrument called the ATAQ-IPF (A Tool to Assess QOL in IPF) version 1

Methods Questionnaire Development Phase I: Item Development

Development of the ATAQ-IPF began with the con-duct of three focus groups and five in-depth interviews

* Correspondence: swigrisj@njc.org

1 Autoimmune Lung Center and Interstitial Lung Disease Program, National

Jewish Health, 1400 Jackson Street, Denver, Colorado, 80206, USA

© 2010 Swigris et al; licensee BioMed Central Ltd This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in

with individual IPF patients, through which we

concep-tualized a framework for describing HRQL in IPF

Details of this step were reported previously [10] We

used themes and whenever possible, exact phrases

spo-ken by focus group members or interviewees to

develop domains and a pool of over 200 total items In

two additional focus groups, each with eight IPF

patients, we reviewed domains (derived from themes)

and items to ensure appropriate wording and coverage

and to make revisions if necessary Reordering

and renaming of the original 12 yielded 14 domains:

Cough, Dyspnea, Forethought, Sleep, Mortality,

Exhaustion, Emotional Well-being, Spirituality, Social

Participation, Finances, Independence, Sexual Health,

Relationships, and Therapies At this stage, the pool

consisted of 207 items All items employed a five-point

Likert response format

Phase II: Domain and Response Category Refinement and

Item Reduction

Next, we enrolled 95 subjects with IPF (89 from the

Interstitial Lung Disease (ILD) clinic at National Jewish

Health and 6 from the ILD clinic at the University of

Pennsylvania) who responded to the 207-item pool IPF

was diagnosed by multi-disciplinary consensus,

accord-ing to internationally accepted guidelines [6] We

sequentially applied a selection criterion (based on

response variance) and Rasch analysis to pare down

items First, items were retained if the sum of the

pro-portion of respondents affirming response options (1)

“Strongly disagree” or (2) “Disagree somewhat” was ≥

25% and options (4)“Agree somewhat” or (5) “Strongly

agree” was ≥ 25% (i.e., 1 + 2 ≥ 25% and 4 + 5 ≥ 25%);

other items were eliminated

Next, separate Rasch analyses[11] were performed on

clusters of retained items within each of the 14

indivi-dual domains and then on the resultant item pool in

its entirety after item elimination at the domain level

In Rasch analysis, a mathematical model is generated

to describe the relationship between respondents and

the items that operationalize a construct (or trait) For

our purposes, for the analyses performed on the

indivi-dual domains, the constructs are implied by the

domain names (e.g., cough, dyspnea, exhaustion, etc.),

and for the analysis of the entire item pool after item

elimination, the over-arching construct is impairment

in HRQL

The Rasch model generates two estimates, called

person location (or logit) and item location (or logit),

which are nonlinear (log odds) transformations of raw

scores The likelihood of higher scores (i.e., person

logit) increases as patients have more of the trait; thus,

for our purposes, respondents with higher scores have

greater impairments in the constructs tapped by the

individual domains or in global HRQL By placing per-son and item logits along opposite sides of a vertical line, in what is called an item map, Rasch analysis reveals how well items target the population under study For dichotomous items (not the case for the ATAQ-IPF), when person and item logits are equal (i.e., directly across from each other on the item map), the person has a 50% probability of affirming the item

A respondent with more of a trait–thus, greater person logit–would be expected to affirm any item with a logit less than his person logit For polytomous items, like those from the ATAQ-IPF, the analysis generates logit positions at the transitions between any adjacent response options (e.g., where the likelihood of responding “Strongly agree” is greater than the likeli-hood of responding to the adjacent option “Agree somewhat” and so-on) If requirements of the Rasch model are met, the scale (here, this holds for the indi-vidual domains and for the instrument in its entirety) will have additive measurement properties, or “behave like a ruler” [12]

There are no absolute criteria, but perhaps the most commonly used measure of item fit to the Rasch model–and the one we employed–is the infit mean square statistic We identified items that both fit the Rasch model (infit mean square statistic 0.5-1.5 is con-sidered useful for measurement[13]) and adequately cov-ered the range of person locations according to the item map Because having multiple items at the same logit position does not substantially add to a questionnaire’s capacity to distinguish respondents with differing levels

of the trait under study, we deleted excess items clus-tered at the same logit position In sum, for paring down items, we followed these steps: 1) examination of item response variance and deletion of items that did not meet the criterion; 2) Rasch analysis on clusters of items within each domain and deletion of poor-fitting

or redundant items; and 3) Rasch analysis of all retained items to ensure fit to the Rasch model and to generate statistics for the instrument as a whole

Psychometric Testing of ATAQ-IPF items

We used Pearson correlation coefficients to examine associations between domain scores and between scores for each domain and all other items in aggre-gate (exclusive of the domain under study) We assessed internal consistency reliability of each domain and the entire instrument with Cronbach’s coefficient alpha [14] Experts suggest alpha should be 0.7-0.9 for subscales of a multi-dimensional questionnaire,[15] with goal values of 0.9 for individual placement and ≥ 0.7 for research purposes [16] Rasch model reliability was assessed by using the reliability of the person

separation index, similar in its interpretation to

Cron-bach’s coefficient alpha

ATAQ-IPF scores and their associations with clinical

measures

Simple summation scoring is used to produce domain

scores and a total score (range 74-370) Higher scores

correspond to greater impairment

On the day the questionnaire was completed, each

subject performed pulmonary function tests (PFT) and a

six-minute walk test (6MWT) PFT were performed

according to American Thoracic Society standards, and

results are reported as percentages of the predicted

values (e.g., FVC% or DLCO%) [17,18] The 6MWT was

conducted as described previously, and distance walked

(6MWD) was recorded [19] Variables were tested for

normality by using the Shapiro-Wilk test Pearson (for

normally distributed variables) or Spearman (for

non-normally distributed variables) correlation was used to

test the null hypothesis of no association between FVC

%, DLCO%, or 6MWD and ATAQ-IPF domain and

total scores We also used multivariable linear regression

to examine the relationship between the ATAQ-IPF

total score and both FVC% and DLCO% We used t

tests (for normally distributed variables) or the

Wil-coxon rank-sum test (for non-normally distributed

vari-ables) to compare mean ATAQ-IPF scores between

subjects using versus not using supplemental oxygen

We hypothesized scores would be higher (more

impair-ment in HRQL) for subjects requiring suppleimpair-mental

oxygen

Statistical Issues

Winsteps version 3.69.1.14 http://www.Winsteps.com was

used to perform the Rasch analyses SAS version 9.2 (SAS,

Inc.; Cary, NC) was used to run all other statistics We

consideredp < 0.05 as statistically significant This project

complied with the Helsinki Declaration Each subject

signed an informed consent, and the study protocol was

approved by the Institutional Review Boards of the

Uni-versity of Pennsylvania and National Jewish Health

Results

Baseline characteristics

Table 1 displays baseline demographic and disease

para-meters (including ATAQ-IPF scores) for the study

sam-ple The mean time from diagnosis to questionnaire

completion was 2.9 years Just over 60% of the sample

used supplemental oxygen, and mean physiology values

suggested moderately severe IPF

Table 1 Baseline Characteristics of Subjects

Ethnicity, %

Smoking status, %

Time since diagnosis, yrs 2.9 (2.8) Using supplemental O2, %

Taking IPF medications, %

Carries a diagnosis of _, %

Item reduction

After the final two focus groups, the questionnaire had

207 items On average, 40 minutes were required to

respond to those items After implementing the

selec-tion criterion based on item variance, 91 items were

dropped, leaving 125 items for the Rasch analyses

(Fig-ure 1) The Finances, Sexual Health, Relationships, and

Therapies domains were left with fewer than six items

after the selection criterion To perform a robust Rasch

analysis on each of these domains, we included all their

candidate items, even though some did not meet the

variance criterion An example of an item map for the

Independence domain is displayed in Figure 2

Domain-total correlations were statistically significant for every domain except Therapies On balance, internal consistency reliability of the domains and overall instru-ment was excellent, and Rasch model reliability of per-son separation was good (Table 2) All retained items fit the Rasch model Because of poor fitting items, the Spirituality domain and its items were dropped from the questionnaire, leaving 13 domains for the ATAQ-IPF version 1

Correlations with lung function and functional status

We observed significant correlations between measures

of pulmonary physiology or functional capacity and ATAQ-IPF domain or total scores (Table 3) FVC% and DLCO% were significantly correlated with eight and nine respectively of the 13 ATAQ-IPF domain scores evaluated, as well as with the ATAQ-IPF total score The 6MWD was significantly correlated with five domain scores as well as the ATAQ-IPF total In a linear regression model of the ATAQ-IPF total score that included FVC% and DLCO% as predictors, FVC%

Cough Dyspnea Forethought Sleep Mortality Exhaustion Emotional

24 24 8 8 22 18 Well-being

37

Spirituality Social Finances Independence Sexual Relationships Therapies

5 Participation 6 11 Health 13 12

6 3

Items = 207

Apply item variance criterion

Cough Dyspnea g y p Forethought Sleep Mortality Exhaustion Emotional g p y

17 12 8 6 7 13 Well-being

19

Spirituality Social Finances Independence Sexual Relationships Therapies

5 Participation 5 8 Health 6 6

Items = 125

Rasch analysis

Cough Dyspnea Forethought Sleep Mortality Exhaustion Emotional

6 6 5 6 6 5 Well-being

7

Social Finances Independence Sexual Relationships Therapies

Items = 74

Social Finances Independence Sexual Relationships Therapies Participation 6 5 Health 6 6

5 5

Figure 1 Sequence of item reduction.

Table 1 Baseline Characteristics of Subjects (Continued)

Data presented as % or mean (standard deviation); O2 = oxygen; FVC% =

percent predicted forced vital capacity; DLCO% = percent predicted diffusing

capacity of the lung for carbon monoxide; COPD = chronic obstructive

pulmonary disease; HRCT = high-resolution computed tomography scan; PH =

pulmonary hypertension; CAD = coronary artery disease

g p p

LOGIT SCALE PERSONS ITEMS

Less Independent More difficult to agree with

(i.e., more difficult to respond Strongly Agree)

2 +

|

|

|

|

| |

| Give up control(4-5) |

|

|

|

| Feel like burden(4-5) | Rearrange(4-5) X | 1 + Frustrated(4-5) |

|

|

X T| Ask for help(4-5) X | |

XXX |T Give up control(3-4) |

XXX | XXX | XXXXXXX S|S | XXXXXX | X | Rearrange(3-4) Feel like burden(3-4) XXXX | Give up control(2-3) 0 XXXXX +M XXXXXX | Frustrated(3-4) XXXXXXX | XXXXXXXXXX M| XXX |S Rearrange(2-3) Ask for help(3-4) Feel like burden(2-3) XXXX | XXXX | XXXXXX | Give up control(1-2) XXXX | Frustrated(2-3) |T XXXX | XX S| X | Ask for help(2-3) XX | Rearrange(1-2) Feel like burden(1-2) XXXXXX | |

-1 XX + Frustrated(1-2) |

X T| | Ask for help(1-2) |

X | |

|

|

|

X | |

|

|

|

-2 +

PERSONS ITEMS More Independent Easier to agree with

(i.e., easier to respond Strongly Agree)

Figure 2 Item map for Independence domain X = one subject; M = mean; S = one standard deviation from mean; T = two standard deviations from mean The item positions for the five items in the independence domain appear on the right of the vertical dashed line The person positions appear on the left of the line Recall the five response options: (1)"Strongly disagree ” (2)"Disagree somewhat” (3)"Neither disagree nor agree ” (4)"Agree somewhat” and (5)"Strongly agree.” Each item appears four times at logit positions that mark transitions between adjacent response options The numbers in parentheses connote the adjacent response options Thus, consider “Ask for help(1-2)” at the lowest (easiest) location on the map: this is the location where the likelihood that a subject would respond (2)"Disagree somewhat ” to this item becomes greater than the likelihood he would respond (1)"Strongly disagree ” to this item The most difficult item from this domain (located at the top of the map) is “Give up control.” The map is designed such that mean item location (difficulty) is at 0 logits (notice the “M” on the right side of the vertical line) Mean person location (ability, indicated by the “M” on the left side of the vertical line) is lower on the vertical line (i.e., fewer logits) than the mean item difficulty, thus indicating that item difficulty is slightly greater than person ability.

(estimate = -0.09, p = 0.78) was not an independent

predictor of the ATAQ-IPF total; DLCO% was

(esti-mate = -1.57, p < 0.0001) The R-square value for this

model was 0.25

Differences in ATAQ-IPF scores between subjects not

using vs those using supplemental oxygen

Nine domain scores (including Dyspnea and Exhaustion)

and the ATAQ-IPF total score were significantly greater

for subjects who required supplemental oxygen than for

subjects who did not use supplemental oxygen (Table 4)

Discussion

We have developed the ATAQ-IPF version 1, an

IPF-specific HRQL questionnaire We used direct patient

inquiry to generate an item pool, and we used rigorous

statistical methods to reduce item numbers and

con-struct an instrument that contains items tapping

domains specifically relevant to patients with IPF

In Phase I of item reduction, we deleted items with

skewed response distributions–this serves the goal of

maximizing the power of the ATAQ-IPF to discriminate

between respondents with different degrees of HRQL

impairment–and reduced item numbers by nearly half

We subjected the remaining items (in their domains and

in aggregate) to Rasch analysis The retained items–by virtue of fitting the Rasch model, like all items that fit the Rasch model–are guaranteed to have the same mea-surement characteristics as concrete physical measures (e.g., length or weight) Thus, by incorporating Rasch analysis into the development of the ATAQ-IPF, unlike other HRQL questionnaires for which Rasch methodol-ogy was not used, we can be confident that it adheres to the basic tenet of arithmetic:‘one more unit means the same amount extra, no matter how much we already have’ [20] So, an increase of one point for an ATAQ-IPF domain or total score means the same thing whether a respondent has severely impaired or near-normal HRQL This linearity that the Rasch model con-structs differs from the assumed linearity of classical test theory and much of item response theory–methodolo-gies used to develop the majority of HRQL instruments [21]

By running Rasch analyses on clusters of items formu-lating each domain, we were able to pare down items in

a systematic fashion By dropping poor-fitting items, or certain ones from groups with identical logit positions (that only serve to make the questionnaire longer and not necessarily enhance the ATAQ-IPF’s power to dis-criminate between respondents whose status changes

Table 2 Results of psychometric and Rasch analyses for the domains of the ATAQ-IPF

(p value)

Internal Consistency Reliability* Rasch Model Reliability

(0.0002)

(<0.0001)

(<0.0001)

(<0.0001)

(<0.0001)

(<0.0001)

(<0.0001)

(<0.0001)

(<0.0001)

(<0.0001)

(<0.0001)

(<0.0001)

(0.07)

*Cronbach ’s coefficient alpha

over time), we were able to shorten the length of each

domain

The detailed and carefully executed item reduction

techniques we used have not been implemented in the

development of many other HRQL instruments

Generat-ing content for the ATAQ-IPF, by directly capturGenerat-ing

patients’ perspectives and using them to build the

frame-work (and specific items) of the questionnaire, ensure its

content validity Involving IPF patients in the

develop-ment process ensures that all relevant themes and effects

are tapped It is the incorporation of such perspectives

that makes the ATAQ-IPF uniquely applicable to IPF

patients and not necessarily to patients with other forms

of lung disease Further, including only items that fit the

Rasch model guarantees each of the ATAQ-IPF’s scales

(domain and total) maintain their additive properties To

our knowledge, only one other investigator has used this

type of approach in the development of respiratory

dis-ease-specific HRQL instruments [2,3]

Psychometric testing revealed that domains and the

overall instrument possess excellent internal consistency

reliability [16] Domain-total correlations confirmed that

each domain measures some aspect of the same under-lying construct–HRQL–and that each contributes infor-mation about HRQL unique from the aggregate contribution of the other items The ATAQ-IPF, then, functions like an arithmetic test that has individual sec-tions that assess addition, subtraction, multiplication, and division: the test score portrays overall arithmetic ability but the sections can point to areas in which a student might excel or need additional instruction Like-wise, the ATAQ-IPF overall scores serves as a measure

of global HRQL, and the domain scores can be used to examine more closely the nature of the impact of an intervention on HRQL

The significant correlations between domain scores and FVC%, DLCO%, and 6MWD showed that ATAQ-IPF scores are related to–but also yield their own unique information from–clinically meaningful, com-monly used measures of IPF severity Results from the linear regression analysis add more weight: in a model that controlled for arguably the two most important physiologic measures used to assess IPF patients (FVC% and DLCO%), those measures combined to explain only 25% of the variability (R-square = 0.25) in the ATAQ-IPF total score Thus, there are factors not captured by these physiologic measures that contribute to HRQL in patients with IPF Interestingly, there was moderately strong correlation between DLCO% and the Social Parti-cipation, Independence, and Sexual Health domains, and there were significant correlations between 6MWD and these domains as well as with the Relationships domain These results indicate that gas exchange and functional capacity influence more than simply physical well-being, and they underscore the importance of extending HRQL measures to include such domains in patients with IPF Investigators commonly view significant associations between HRQL scores and clinical measures of disease severity or functional status as evidence for the validity

of an instrument; however, the importance of such associations is primarily in understanding which mani-festations of a disease have the greatest effects on HRQL–they are much less relevant to validity So, although such correlations in this study confirmed our hypotheses that HRQL would be related to IPF severity (as measured by these physiologic variables), the validity

of the ATAQ-IPF (or any other instrument) is best judged over time on three other terms: 1) its content– whether it covers all the relevant dimensions on which individuals evaluate their HRQL, or at least those that might be affected by the disease in question; 2) whether items require respondents to indicate the extent to which their QOL (on the various domains) is compro-mised by their disease; and 3) whether resulting scores are reliable, sensitive, and responsive to change The ATAQ-IPF certainly meets terms 1 and 2, and further

Table 3 Correlations between pulmonary function or

six-minute walk distance and ATAQ-IPF scores

p = 0.01

-0.19

p = 0.08

-0.004

p = 0.98

p < 0.0001

-0.52

p < 0.0001

-0.23

p = 0.09

p = 0.0003

-0.58

p < 0.0001

-0.35

p = 0.009

p = 0.07

-0.1

p = 0.38

-0.18

p = 0.18

p = 0.19

-0.05

p = 0.65

0.05

p = 0.73

p = 0.001

-0.46

p < 0.0001

-0.16

p = 0.26 Emotional Well-being -0.19

p = 0.06

-0.32

p = 0.003

-0.18

p = 0.17 Social Participation -0.21

p = 0.04

-0.51

p < 0.0001

-0.33

p = 0.01

p = 0.98

-0.18

p = 0.12

-0.08

p = 0.58

p = 0.0015

-0.47

p < 0.0001

-0.39

p = 0.004

p = 0.04

-0.55

p < 0.0001

-0.41

p = 0.002

p = 0.006

-0.40

p = 0.0002

-0.40

p = 0.003

p = 0.48

0.21

p = 0.05

0.29

p = 0.03

p = 0.006

-0.52

p < 0.0001

-0.28

p = 0.04 FVC% = percentage of predicted value for forced vital capacity;

DLCO%= percentage of predicted value for diffusing capacity of the lung for

carbon monoxide; 6MWD = total distance walked during six-minute timed

walk test; N = 95 for FVC, 82 for DLCO, and 54 for 6MWD

investigation will determine term 3 As with any HRQL

questionnaire, validity is not achieved (or even

deter-mined) in a single study–it is built It is only through

observing the performance of a questionnaire in

multi-ple studies over time that we can confidently say that it

measures what it was intended to measure That said,

the results of the analysis in which we examined

differ-ences in ATAQ-IPF scores between subjects not using

and those using supplemental oxygen support the

valid-ity of the ATAQ-IPF: subjects using supplemental

oxygen had more dyspnea and exhaustion, less

indepen-dence, required more forethought, and had greater

impairments in emotional well-being, social

participa-tion, sexual health, relationships, and overall HRQL

(according to the ATAQ-IPF total) than subjects not

using supplemental oxygen

Although 74 items comprise version 1 of the

ATAQ-IPF, this number of items enables it to tap myriad

important constructs and to report scores at the domain

level Whether item number can be reduced further,

without unacceptable loss of content or reliability,

requires additional investigation Moving forward, we

will use the ATAQ-IPF as a secondary outcome measure

in a longitudinal study, and we invite other investigators

to use the ATAQ-IPF version 1 in their studies as well

Conclusion

In sum, we have developed an IPF-specific instrument

to measure HRQL We used patients’ views to generate

themes and items and then systematically implemented

statistical techniques to pare down item number Items

fit the Rasch model, and internal consistency supported

reporting of domain and total scores In future studies,

data will be gathered to help further support the

ATAQ-IPF’s validity in IPF and to determine if it might

be useful in other forms of interstitial lung disease

Acknowledgements The authors wish to thank and acknowledge Michael Gould, MD, MS; Susan Jacobs, RN, MS; Michael Linacre, PhD; Milton Rossman, MD; Anita Stewart, PhD; David Streiner, PhD; and Janelle Yorke, PhD for their assistance and thoughtful input at various stages of this project.

Author details

1

Autoimmune Lung Center and Interstitial Lung Disease Program, National Jewish Health, 1400 Jackson Street, Denver, Colorado, 80206, USA 2 Palo Alto Medical Foundation Research Institute, Palo Alto Medical Foundation, 795 El Camino Real, Palo Alto, California, 94301, USA 3 Morgridge College of Education, University of Denver, 2199 S University Blvd, Denver, Colorado,

80210, USA 4 Division of Psychosocial Medicine, National Jewish Health, 1400 Jackson Street, Denver, Colorado, 80206, USA.

Authors ’ contributions Study conceptualization: JJS, SW Data collection: JJS, DS, KB Data analysis: JJS, SW, KG, FW Writing and final approval of manuscript: JJS, SW, KG, DS,

KB, FW.

Competing interests JJS is supported in part by a Career Development Award from the NIH (K23 HL092227) The authors declare that they have no competing interests Received: 30 April 2010 Accepted: 31 July 2010 Published: 31 July 2010 References

1 Guyatt G, Walter S, Norman G: Measuring change over time: assessing the usefulness of evaluative instruments J Chronic Dis 1987, 40:171-178.

2 Jones PW, Harding G, Berry P, Wiklund I, Chen WH, Kline Leidy N: Development and first validation of the COPD Assessment Test Eur Respir J 2009, 34:648-654.

3 Meguro M, Barley EA, Spencer S, Jones PW: Development and Validation

of an Improved, COPD-Specific Version of the St George Respiratory Questionnaire Chest 2007, 132:456-463.

4 Juniper EF, O ’Byrne PM, Guyatt GH, Ferrie PJ, King DR: Development and validation of a questionnaire to measure asthma control Eur Respir J

1999, 14:902-907.

5 Hyland ME, Finnis S, Irvine SH: A scale for assessing quality of life in adult asthma sufferers J Psychosom Res 1991, 35:99-110.

Table 4 Comparison of ATAQ-IPF scores between subjects using vs not using supplemental oxygen

N = 37

Using supplemental O2

N = 58

P value

6 American Thoracic Society: Idiopathic pulmonary fibrosis: diagnosis and

treatment International consensus statement American Thoracic Society

(ATS), and the European Respiratory Society (ERS) Am J Respir Crit Care

Med 2000, 161:646-664.

7 Olson AL, Swigris JJ, Lezotte DC, Norris JM, Wilson CG, Brown KK: Mortality

from pulmonary fibrosis increased in the United States from 1992 to

2003 Am J Respir Crit Care Med 2007, 176:277-284.

8 Swigris JJ, Gould MK, Wilson SR: Health-related quality of life among

patients with idiopathic pulmonary fibrosis Chest 2005, 127:284-294.

9 Swigris JJ, Kuschner WG, Jacobs SS, Wilson SR, Gould MK: Health-related

quality of life in patients with idiopathic pulmonary fibrosis: a

systematic review Thorax 2005, 60:588-594.

10 Swigris JJ, Stewart AL, Gould MK, Wilson SR: Patients ’ perspectives on how

idiopathic pulmonary fibrosis affects the quality of their lives Health

Qual Life Outcomes 2005, 3:61.

11 Rasch G: Probabilistic models for some intelligence and attainment tests.

Danish Institute of Educational Research 1960.

12 Bond T, Fox C: Applying the Rasch Model: Fundamental Measurement in the

Human Sciences Mahway, New Jersey: Lawrence Erlbaum Associates 2007.

13 Linacre J: What do Infit and Outfit, Mean-square and Standardized

mean? Rasch Measurement Transactions 2002, 16:878.

14 Cronbach L: Coefficient alpha and the internal structure of tests.

Psychometrika 1951, 22:293-296.

15 Streiner D, Norman G: Health Measurement Scales: A practical guide to their

development and use New York: Oxford University Press, Fourth 2008.

16 Nunnally J: Psychometric Theory New York: McGraw-Hill 1978.

17 American Thoracic Society: Lung function testing: selection of reference

values and interpretative strategies Am Rev Respir Dis 1991,

144:1202-1218.

18 American Thoracic Society: Standardization of spirometry, 1994 update.

Am J Respir Crit Care Med 1995, 152:1107-1136.

19 Swigris JJ, Swick J, Wamboldt FS, et al: Heart Rate Recovery After 6-Minute

Walk Test Predicts Survival in Patients With Idiopathic Pulmonary

Fibrosis Chest 2009, 136:841-848.

20 Linacre J: Measurement, Meaning and Mortality Pacific Rim Objective

Measurement Symposium and International Symposium on Measurement and

Evaluation Kuala Lumpur, Malaysia 2005.

21 Linacre JM: Lesson 1 Pracitcal Rasch Measurement - Core Topics 2010 [http://

www.statistics.com].

doi:10.1186/1477-7525-8-77

Cite this article as: Swigris et al.: Development of the ATAQ-IPF: a tool

to assess quality of life in IPF Health and Quality of Life Outcomes 2010

8:77.

Submit your next manuscript to BioMed Central and take full advantage of:

• Convenient online submission

• Thorough peer review

• No space constraints or color figure charges

• Immediate publication on acceptance

• Inclusion in PubMed, CAS, Scopus and Google Scholar

• Research which is freely available for redistribution

Submit your manuscript at www.biomedcentral.com/submit